The computer implemented method and system disclosed herein, in general, relates to an object identification system. More particularly, the computer implemented method and system disclosed herein relates to identification of circular objects in an image.
The identification of circular objects in images has conventionally been carried out using a circular Hough transform (CHT) on binary images. The circular Hough transform is a specialized type of a conventional Hough transform for identifying circular objects in an image. A conventional Hough transform relies on the detection of edges in the image. Therefore, the efficiency of the Hough transform is dependent on the accuracy of detection of the edges in an image. Conventional Hough transform (HT) based techniques typically consider edges in the image and accumulate votes only in locations which provide good contrast in order to avoid false detections. However, for low contrast images, the HT based techniques are often not effective for identifying circular objects since the images are hard to binarize through edge detection in low contrast images. Consider an example of an image of a vehicle tire. The efficiency of the Hough transform is reduced in this case since vehicle tires typically do not have a good contrast with respect to a road on which they are driven, particularly during night time or in dimly lit areas, and the vehicle tires are less likely to stand out in an edge detected image.
Furthermore, in a noisy image of a real circular object, the method of accumulation of votes at the center point of the circular object as suggested by the Hough transform triggers false peaks at low contrast locations in the image. To override this problem, some techniques for identifying circular objects in an image employ gradients in gray scale images to accumulate votes in an accumulator, as in the standard Hough transform. However, these techniques are constrained by the complexity of features in a natural image and often record a lot of peaks and false detections.
Furthermore, the methods for identifying circular objects in an image based on the conventional Hough transform typically resort to obtaining gradients at each of the points on the circular object during processing of a low contrast image. However, in the case of low contrast images, the direction of the gradient at a particular pixel point, that is, whether the gradient points inward or outward, cannot be directly determined. For example, if the radius of the circular object is R, conventional HT based algorithms increment the accumulator at a distance of R in either of the directions resulting in a large number of votes in the accumulator at the center point of the circular object and a relatively small number of votes at a distance of 2R from the center point. Therefore, since the direction along which the actual center point of the circular object is located is not definitive, methods using this algorithm accumulate votes along both the inward and outward directions to include all possibilities, thereby resulting in false detections. Furthermore, in a low contrast noisy image of a real object, there is a possibility of accumulation of votes even at points that do not qualify as the center point of the circular object. For example, the votes contributed by noisy pixels points in a low contrast image result in the generation of a false peak at a low contrast location in the image. This leads to a possibility that the method may fail to identify the actual circular objects, for example, vehicle tires, in the image.
Another method for identifying circular objects in the image comprises the use of edge magnitude based algorithms that are based on determination of the difference in the gradient magnitude between the pixel points in an image. However, methods using conventional edge magnitude based algorithms for identification of circular objects are also not effective for low contrast images since the gradient magnitude difference between the pixel points in the image is low. Another method for identifying circular objects in the image is restricted to verifying the convergence of gradient directions of all the pixel points in the image to a particular pixel point that is a prospective center point of the circular object in the image. However, this method is not effective for low contrast images due to the ambiguity resulting from the intersection of multiple gradient directions of all the pixel points throughout the image.
Another method for identifying circular objects in low contrast images comprises using symmetric gradient pair vectors at diametrically opposing points. This method identifies candidate circles for each pair of gradient vectors with the center point of a candidate circle equal to the midpoint of the line connecting the pair of gradient vectors, and accumulates votes for each of the candidate circles. Although this method is directed at improving the speed of identification of circular objects in the image, the method is constrained by a mandatory requirement that the circular object be completely visible in the image. Moreover, this method is limited by an inability to identify semi-circular objects, arcuate objects, occluded objects, etc. Furthermore, this method is constrained by a limited ability to search and locate the diametrically opposing points for a non-rigid circular object such as a vehicle tire, which leads to multiple false detections.
Furthermore, consider a practical application where identification of circular objects in an image can be used to perform classification of vehicles based on the identification of vehicle tires in real time. Conventionally, induction loop systems have been used for identifying the characteristics of vehicles and classifying the vehicles based on the identified characteristics. Induction loop systems require the installation of an insulated, electrically conducting loop under the road for detection of vehicles. The cost of installation of induction loop systems is substantially higher since induction loop systems demand the use of multiple sensitive sensors in a single location. Moreover, induction loop systems need appropriate mounting locations for installation and need to be installed each time a road is repaved. Induction loop systems need constant supervision and repair, further increasing the time and cost of maintenance, and also require a lot of ground work on a periodic basis. Therefore, there is a need for a non-intrusive technology such as an image processing technology that imposes lesser hardware constraints, and provides the flexibility of performing identification and classification of vehicles in real time.
Conventional image processing technologies typically employ image sensors and imaging based object detection, herein referred to as “vision based methods”. The vision based methods analyze the images by extracting features from the image, that is, a set of predetermined shapes and points that characterize a specific aspect of the object. In an example, the tires of the vehicle that characterize the vehicle can be identified by the image sensors and analyzed for classifying the vehicle. However, conventional vision based methods often add to the complexity and computing power needed for detection of an object in an image. Moreover, conventional vision based methods have often been found sensitive to changing light conditions and consequently are ill-equipped to process low contrast images. Furthermore, since conventional vision based methods are reliant on precise positioning of image sensors, the efficiency of these vision based methods is reduced when detecting occluded objects in the image.
Hence, there is a long felt but unresolved need for a computer implemented method and system that optimally determines a center point of a circular object and identifies one or more circular objects in a low contrast image. Moreover, there is a need for a faster, efficient computer implemented method and system that reduces the number of computations required for identifying a circular object in an image by accumulating votes only at valid center points in the image. Furthermore, there is a need for an optimal computer implemented method and system that identifies a circular object in a low contrast image and enables a faster identification of vehicle tires and subsequent classification of the vehicle based on the characteristics of the vehicle tires.